where \(\lambda \)  corresponds to \(n\)  different compression rates, \(R\)  is referred to as the expected length of the compressed bitstream, and \(D\)  is the expected distortion of the reconstructed image concerning the original image measured by either mean squared error (MSE) or MS-SSIM.
Drawing lessons from [1]}, conditional convolutions are added into the autoencoder network [2]} for the variable-rate control in the unified auto-encoder model.
Apart from the input frame, the unified auto-encoder model also takes \(\lambda \)  as input to enable the conditional convolutions and produces a compressed image with varying rates.
